class torch.nn.AvgPool2d(kernel_size: Union[T, Tuple[T, T]], stride: Optional[Union[T, Tuple[T, T]]] = None, padding: Union[T, Tuple[T, T]] = 0, ceil_mode: bool = False, count_include_pad: bool = True, divisor_override: bool = None)[source]

Applies a 2D average pooling over an input signal composed of several input planes.

In the simplest case, the output value of the layer with input size (N,C,H,W)(N, C, H, W) , output (N,C,Hout,Wout)(N, C, H_{out}, W_{out}) and kernel_size (kH,kW)(kH, kW) can be precisely described as:

out(Ni,Cj,h,w)=1kHkWm=0kH1n=0kW1input(Ni,Cj,stride[0]×h+m,stride[1]×w+n)out(N_i, C_j, h, w) = \frac{1}{kH * kW} \sum_{m=0}^{kH-1} \sum_{n=0}^{kW-1} input(N_i, C_j, stride[0] \times h + m, stride[1] \times w + n)

If padding is non-zero, then the input is implicitly zero-padded on both sides for padding number of points.

The parameters kernel_size, stride, padding can either be:

  • a single int – in which case the same value is used for the height and width dimension

  • a tuple of two ints – in which case, the first int is used for the height dimension, and the second int for the width dimension

  • kernel_size – the size of the window

  • stride – the stride of the window. Default value is kernel_size

  • padding – implicit zero padding to be added on both sides

  • ceil_mode – when True, will use ceil instead of floor to compute the output shape

  • count_include_pad – when True, will include the zero-padding in the averaging calculation

  • divisor_override – if specified, it will be used as divisor, otherwise kernel_size will be used

  • Input: (N,C,Hin,Win)(N, C, H_{in}, W_{in})

  • Output: (N,C,Hout,Wout)(N, C, H_{out}, W_{out}) , where

    Hout=Hin+2×padding[0]kernel_size[0]stride[0]+1H_{out} = \left\lfloor\frac{H_{in} + 2 \times \text{padding}[0] - \text{kernel\_size}[0]}{\text{stride}[0]} + 1\right\rfloor
    Wout=Win+2×padding[1]kernel_size[1]stride[1]+1W_{out} = \left\lfloor\frac{W_{in} + 2 \times \text{padding}[1] - \text{kernel\_size}[1]}{\text{stride}[1]} + 1\right\rfloor


>>> # pool of square window of size=3, stride=2
>>> m = nn.AvgPool2d(3, stride=2)
>>> # pool of non-square window
>>> m = nn.AvgPool2d((3, 2), stride=(2, 1))
>>> input = torch.randn(20, 16, 50, 32)
>>> output = m(input)


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